{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.017531Z",
     "start_time": "2025-05-03T08:02:47.319452Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.062042Z",
     "start_time": "2025-05-03T08:02:52.030751Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 1. 加载数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]  # 只取前两个特征便于可视化\n",
    "y = iris.target#代表样本中类别标签"
   ],
   "id": "b1a303aed909401c",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.108574Z",
     "start_time": "2025-05-03T08:02:52.104486Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#过率标签\n",
    "X=X[y!=2]\n",
    "y=y[y!=2]"
   ],
   "id": "b7d979d5daca87c5",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.144499Z",
     "start_time": "2025-05-03T08:02:52.138855Z"
    }
   },
   "cell_type": "code",
   "source": "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)",
   "id": "9b5af556d68edc70",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.174888Z",
     "start_time": "2025-05-03T08:02:52.157305Z"
    }
   },
   "cell_type": "code",
   "source": [
    "clf=SVC(kernel='linear',C=1.0,)\n",
    "clf.fit(X_train,y_train)"
   ],
   "id": "335ae09639012281",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.257252Z",
     "start_time": "2025-05-03T08:02:52.253433Z"
    }
   },
   "cell_type": "code",
   "source": "y_pred=clf.predict(X_test)",
   "id": "f0acedc368f9df2e",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.292726Z",
     "start_time": "2025-05-03T08:02:52.281479Z"
    }
   },
   "cell_type": "code",
   "source": "print(\"分类报告：\\n\",classification_report(y_test,y_pred))",
   "id": "7677578f608b31c0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.407703Z",
     "start_time": "2025-05-03T08:02:52.400242Z"
    }
   },
   "cell_type": "code",
   "source": "print('混淆矩阵：\\n',confusion_matrix(y_test,y_pred))",
   "id": "13d7258f9cc478e9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "混淆矩阵：\n",
      " [[17  0]\n",
      " [ 0 13]]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.440054Z",
     "start_time": "2025-05-03T08:02:52.435492Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_decision_bounddary(clf,X,y):\n",
    "    h=.02\n",
    "    x_min,x_max=X[:,0].min()-1,X[:,0].max()+1\n",
    "    y_min,y_max=X[:,0].min()-1,X[:,0].max()+1\n",
    "    xx,yy=np.meshgrid(np.arange(x_min,x_max,h),np.arange(y_min,y_max,h))\n",
    "    Z=clf.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "    Z=Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "    plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.coolwarm,edgecolors='k')\n",
    "    plt.xlabel('Sepal length')\n",
    "    plt.ylabel('Sepal width')\n",
    "    plt.title('SVM Decision Boundary')\n",
    "    plt.show()"
   ],
   "id": "d6765b9e20225496",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-03T08:02:52.871878Z",
     "start_time": "2025-05-03T08:02:52.451348Z"
    }
   },
   "cell_type": "code",
   "source": "plot_decision_bounddary(clf, X, y)",
   "id": "23031914331d28af",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
